code: def test_when_mlf_model_is_used_then_predictions_have_correct_scale(
      temp_model_path, model_type, eval_metric
  ):
      prediction_length = 5
      value = 2e6
      data = TimeSeriesDataFrame.from_iterable_dataset(
          [
              {
                  "start": pd.Period("2020-01-01", freq="D"),
                  "target": np.random.normal(
                      loc=value, scale=10, size=[30]
                  ),
              }
          ]
      )
      model = model_type(
          path=temp_model_path,
          freq=data.freq,
          eval_metric=eval_metric,
          quantile_levels=[0.1, 0.5, 0.9],
          prediction_length=prediction_length,
      )
      model.fit(train_data=data)
      predictions = model.predict(data)
      assert np.all(np.abs(predictions.values - value) < value)
  
file_path: autogluon/timeseries/tests/unittests/models/test_mlforecast.py
imports: from unittest import mock
  import numpy
  import pandas
  import pytest
  from autogluon.timeseries import TimeSeriesDataFrame
  from autogluon.timeseries.models.autogluon_tabular.mlforecast import DirectTabularModel, RecursiveTabularModel
  from autogluon.timeseries.utils.features import TimeSeriesFeatureGenerator
  from autogluon.timeseries.utils.forecast import get_forecast_horizon_index_ts_dataframe
  from ..common import DATAFRAME_WITH_COVARIATES, DATAFRAME_WITH_STATIC, DUMMY_VARIABLE_LENGTH_TS_DATAFRAME, get_data_frame_with_variable_lengths
label_desc: Use image node_id 9 for calling a global function with example usage: test_when_mlf_model_is_used_then_predictions_have_correct_scale(temp_model_path, model_type, eval_metric) without return types